Heat shock response (HSR), in terms of transcription regulation of two heat shock proteins genes hsp70 and hsp90), was analysed in a widespread tropical copepod Pseudodiaptomus annandalei. The mRNA transcripts of both genes were quantified after copepods at a salinity of 20 underwent an acclimation process involving an initial acclimation temperature of 29 °C, followed by gradual thermal ramping to the target exposure temperature range of 24-36 °C. The respective cellular HSR and organismal metabolism, measured by respiratory activity at exposure temperatures, were compared. The fold change in mRNA expression for both hsp70 and hsp90 (8-9 fold) peaks at 32 °C, which is very close to 32.4 °C, the upper thermal optimum for respiration in the species. Unexpectedly, the modelled HSR curves peak at only 3 °C (hsp90) and 3.5 °C (hsp70) above the mean water temperature (29.32 °C) of the copepod in the field. We propose that copepods in tropical waters adopt a preparative HSR strategy, early at the upper limit of its thermal optimum, due to the narrow thermal range of its habitat thus precluding substantial energy demand at higher temperatures. However, the model suggests that the species could survive to at least 36 °C with short acclimation time. Nevertheless, the significant overlap between its thermal range of hsp synthesis and the narrow temperature range of its habitat also suggests that any unprecedented rise in sea temperature would have a detrimental effect on the species.
Cardiovascular disease is a major global health issue. In particular, acute myocardial infarction (AMI) requires urgent attention and early diagnosis. The use of point-of-care diagnostics has resulted in the improved management of cardiovascular disease, but a major drawback is that the performance of POC devices does not rival that of central laboratory tests. Recently, many studies and advances have been made in the field of surface-enhanced Raman scattering (SERS), including the development of POC biosensors that utilize this detection method. Here, we present a review of the strengths and limitations of these emerging SERS-based biosensors for AMI diagnosis. The ability of SERS to multiplex sensing against existing POC detection methods are compared and discussed. Furthermore, SERS calibration-free methods that have recently been explored to minimize the inconvenience and eliminate the limitations caused by the limited linear range and interassay differences found in the calibration curves are outlined. In addition, the incorporation of artificial intelligence (AI) in SERS techniques to promote multivariate analysis and enhance diagnostic accuracy are discussed. The future prospects for SERS-based POC devices that include wearable POC SERS devices toward predictive, personalized medicine following the Fourth Industrial Revolution are proposed.
SERS detects single molecules with exceptional sensitivity. To counter the issue of selectivity faced by point-of-care, herein, an externally applied electric field that allows electrical modulation and electromigrates unbound SERS tags without multiple washing steps is successfully developed and demonstrated to improve the biosensor's selectivity and sensitivity in multiplexed detection of cTnI, HDL, and LDL in human serum at a low LoD. Ultra-sensitive detectors can detect signals from non-specifically absorbed species, and these species can cover up overlapping analyte peaks, amplifying the effect of non-specific binding. Even though antifouling molecules can prevent non-specific adsorption at the sensor interface, this approach does not completely eliminate it. Our significant findings show that an electrically regulated device can electromigrate non-specifically bound species without cross-reacting with endogenous albumin proteins. Stability, repeatability, and reproducibility were good, with an RSD of 10%. Artificial intelligence was employed to interpret and analyze high-dimensional fingerprint SERS spectra using feature selection and dimensionality reduction for accurate acute myocardial infarction diagnosis and prognosis. These machine learning methods allow quantification of cTnI, HDL, and LDL biomarkers with low RMSE. Machine learning classifiers showed strong AUROC values of 0.950 ± 0.111 and 0.884 ± 0.139 for early and recurrent AMI detection, respectively. A high negative predictive value (NPV) of ≥99% indicates an effective early AMI rule-out. In short, this work demonstrated that a simple, low-cost, electrophoretic modulated biosensor with machine learning can diagnose, rule out, and predict recurring AMI.